The influence of gender beliefs and early exposure to math, science and technology in female degree choices

The influence of gender beliefs and early exposure to
math, science and technology in female degree choices




Laura Cristina Rojas Blanco

PhD



The University of York

Politics, Economics and Philosophy


July, 2013

2

Abstract

This research consists of three sections testing the hypothesis that gender roles and
gender-stereotyping of certain fields of study could be associated with women choosing
traditionally female degree options characterized by lower wages. The analysis is framed

within the identity economics framework. In the first chapter, data from the 1970 British
Cohort Study supports the hypothesis that teenage girls are more likely to accept gender-equal
beliefs when their mother shares these beliefs or she works; and that having gender equal
beliefs and developing early mathematical and technological skills either encourage girls to
study for high-paying degrees or discourage them from entering female-dominated degrees.

The second chapter analyses the responses from an online questionnaire applied to
female academics at the University of York. Such survey collected testimonies about their
experiences regarding the construction of gender, encouragement and discouragement in
mathematics, science and technology at school and the household environments; and their
degree choice. Results provide some evidence in favour of the initial hypothesis, but they also
show a disassociation between how women perceive the sex-typing of subject fields and their
own confidence in their capabilities and tastes. It also suggests that bad experiences with
certain subjects are more relevant in keeping women away from high-earnings degrees than
the lack of positive experiences.

Finally, the third chapter estimates earnings functions and provides a gender wage
decomposition using data from the 1970 British Cohort Study at ages 29 and 34. Results do
not support the hypothesis that having a high-earnings degree is associated with higher wages
for women. Although there is an initial premium, it disappears by age 34. In contrast, working
in a high-earnings occupation is positively associated with higher wages, while remaining in
female-dominated occupations is negatively associated with wages for women.


3

List of contents

Abstract 2
List of contents 3

List of tables 6
List of figures 8
List of graphics 9
Acknowledgements 10
Author’s declaration 11
1. Introduction 12
2. Literature review 21
2.1. The human capital model 22
2.2. Discrimination and the gender wage gap 28
2.2.1. Empirical literature 32
2.3. Occupational segregation and female labour participation 36
2.4. Subject choice within education and the wage gap 44
2.5. Skill-bias technological change and the wage gap 46
2.6. Identity economics and gender roles 49
2.7. Conclusion 64
3. Getting a ‘girlie’ education: gender beliefs and early mathematical and technological
stimuli in female degree choices 65
3.1. Introduction 65
3.2. Model 70
3.3. Dataset: 1970 British Cohort Study 74
3.4. Construction of gender identity 76
3.4.1. Variable description 76
3.4.2. Model specification 85
3.4.3. Probit estimation results for believing that women can do the same job as
men 86
3.4.4. Probit estimation results for believing in gender equality in sex and marriage
92
3.4.5. Ordinary least square estimation results for gender equality in sex and
marriage score 96
3.5. Degree choice 99

3.5.1. Variable description 99
4

3.5.2. Model specification 104
3.5.3. Estimation results for degree choice 105
3.6. Conclusion 112
4. A mixed methods approach to female degree choices 114
4.1. Introduction 114
4.2. Dataset 116
4.3. Descriptive analysis 119
4.3.1. Degree choice 120
4.3.1.1. Reasons for choosing a degree 120
4.3.1.2. Role models and others’ influence 130
4.3.2. School environment 136
4.3.2.1. Teachers’ behaviour towards math, science and technology 140
4.3.2.2. Participation in extracurricular activities 146
4.3.2.3. Remarks on school environment 148
4.3.3. Household environment 150
4.3.3.1. Toys 151
4.3.3.2. Technological confidence 155
4.3.3.3. Parents’ behaviour towards math, science and technology 156
4.3.3.4. Parental aspirations 162
4.3.4. Beliefs and personal views 165
4.3.5. Satisfaction 172
4.4. A model of degree choice 177
4.4.1. Model 177
4.4.2. Findings 180
4.5. Conclusions 183
5. Exploring the correlation between degree and occupational choice with the earnings
function and gender wage gap decomposition 187

5.1. Introduction 187
5.2. Methodology 190
5.2.1. Human capital model 191
5.2.2. Augmented human capital model 193
5.2.3. Variable inclusion model 194
5.2.4. Full model 195
5.2.5. Wage decomposition 195
5.3. Data discussion 196
5

5.4. Results 206
5.4.1. Probability of working for women 206
5.4.2. Human capital model 208
5.4.3. Augmented human capital model 212
5.4.4. Results for high-earnings degrees 217
5.4.5. Results for female-dominated degrees 219
5.4.6. Results for high-earnings occupations 221
5.4.7. Results for female-dominated occupations 223
5.4.8. Full model 225
5.4.9. Wage decomposition 229
5.5. Conclusions 235
6. Concluding remarks 237
7. Appendices 248
Appendix 1: Observations per variable used in estimating gender identity and degree
choice, as percentage of cohort size 249
Appendix 2: Subject fields of study classified as traditionally female 252
Appendix 3: Degree subject fields associated with high earnings 253
Appendix 4: Online questionnaire 254
Appendix 5: Observations per variable used in estimating earnings, as percentage of
cohort size 267

Appendix 6: Standard Occupational Classification codes (1990) classified as traditionally
female 269
Appendix 7 Standard Occupational Classification codes (1990) associated with high
earnings 270
8. References 271



6

List of tables

TABLE 1: Science and Engineering graduate students in the USA, by field of study and sex. Year:
2008 13
TABLE 2: Correlations observed between 3-point Likert attitudinal variables regarding gender
77
TABLE 3: Correlations observed between 5-point Likert scale maternal attitudinal variables
regarding gender 81
TABLE 4: Summary of descriptive statistics related to gender identity, by respondent's sex 84
TABLE 5: Probit estimates for believing women can do the same job as men 90
TABLE 6: Probit estimates for believing in gender equality regarding sex and marriage 94
TABLE 7: Ordinary least squares estimates for gender equality in sex and marriage index 97
TABLE 8: Summary of descriptive statistics related to degree choice, by respondent's sex 102
TABLE 9: Probit estimates for degree choice 110
TABLE 10: Educational level and degree types 119
TABLE 11: Main reason to choose degree program, by degree choice 121
TABLE 12: Correlation coefficients between degree choices and possible reasons to study a
particular program 122
TABLE 13: Participation in extracurricular activities and its correlation with degree choice 147
TABLE 14: Correlation coefficients between academic ability and degree choice 149

TABLE 15: Toys frequently played with during childhood and its correlation with degree
choices 152
TABLE 16: Parental aspirations 163
TABLE 17: Distribution of level of agreement with gender stereotype statements and its
correlation with degree choice 166
TABLE 18: Distribution of parental beliefs 168
TABLE 19: Correlation coefficients for respondent's and parental beliefs 169
TABLE 20: Satisfaction and its correlation with degree choice 173
TABLE 21: Descriptive statistics for the balanced sample, by age group 179
TABLE 22: Average marginal effects of the binary response models on degree choice 183
TABLE 23: Descriptive statistics for graduates in the 1970 BCS, by wave 204
TABLE 24: Probit results for working graduate women 207
7

TABLE 25: Earnings functions according to the human capital model, with and without
Heckman sample selection correction 209
TABLE 26: Earnings functions according to the augmented human capital model 215
TABLE 27: Earnings functions, including holding a high-earnings degree into the model 218
TABLE 28: Earnings functions, including holding a female-dominated degree into the model
220
TABLE 29: Earnings functions, including working in a high-earnings occupation into the model
222
TABLE 30: Earnings functions, including working in a female-dominated occupation into the
model 224
TABLE 31: Earnings functions, full model 225
TABLE 32: Gender wage gap decomposition 234


8

List of figures

FIGURE 1: Tree game payoffs 73
FIGURE 2: Concept map for degree choice 186




9

List of graphics

GRAPHIC 1: Percentage of women employed for some gender segregated occupations in the
United States. Year: 2009………………………………………………………………………………………………………14
GRAPHIC 2: Mean and median annual wage for some gender segregated occupations in the
United States. Year: 2009………………………………………………………………………………………………………15
GRAPHIC 3: Graduate qualifications obtained on high education institutions in the United
Kingdom, by gender and subject area. Year: 2009-2010……………………………………………………… 17
GRAPHIC 4: Mean salaries for graduate in the United Kingdom, by gender and subject area.
Year: 2009-2010…………………………………………………………………………………………………………………… 18
GRAPHIC 5: Occupational destination of graduates employed in the UK, by gender. Year:
2010……………………………………………………………………………………………………….………………………………19
GRAPHIC 6: Sexuality index score distribution, by gender …………………………………… ………………79
GRAPHIC 7: Maternal gender equality index score distribution, by cohort member's gender…82
GRAPHIC 8: Gross log wage distribution, by gender…………………………………………………………… 200




10

Acknowledgements


A special thanks to my supervisor, Professor Karen Mumford for her constant support and
comments. I am also grateful to Professor Stevi Jackson, Professor Jonathan Bradshaw, Emma
Tominey and Professor Sarah Brown for their comments; to Alison Watson and to my family
and friends for their emotional support.

I am grateful to the University of York for awarding me with an Overseas Research Scholarship,
without which I would have never been able to study my PhD, and to Universidad de Costa
Rica for funding me through their credit scheme to study abroad.



11

Author’s declaration


I hereby declare that the work presented in this dissertation is my own and belongs to the
research carried out as a student at the University of York from October, 2010 to the present
day.



12

The influence of gender beliefs and early exposure to math, science
and technology in female degree choices

“One might ask: if an education geared to the growth of
the human mind weakens femininity, will an education
geared to femininity weaken the growth of the mind?
What is femininity, if it can be destroyed by an
education which makes the mind grow, or induced by
not letting the mind grow?” (Friedan, 1963, p. 136)

1. Introduction

On average, working women earn about three quarters of the male wage (United
Nations Statistics Division, 2010). This gender wage gap constitutes a persistent disadvantage
for working women, who cannot access the same wages as their male counterparts.
Occupational segregation
1
stands out as the most significant barrier in closing the gender wage
gap (Becker, 1971 and Oaxaca, 1973). Although the wage differential between women and
men narrowed during the 1990s, its persistence proves difficult to explain from standard
economic theory: in the short-run, because human capital is fixed, an excess demand for one
type of workers would push their wages up but, in time, this higher wage would create an
incentive for the workers in the other sector to invest enough in their human capital in order
to mobilize to the other, more dynamic one. Eventually, this would increase the labour supply
in the first sector and reduce it in the second one, so that the market wages would tend to
converge again. However, this has not happened: women do not enter the occupations that
offer higher economic possibilities at the pace needed to keep narrowing –and eventually
close- the income gap. Most of the literature that has looked into this problem focuses on
entry barriers or discrimination on part of the firms or the male workers. Instead, this
research focuses on female behaviour. In particular, it looks at different social factors that
might influence the degree choice of graduate women in the United Kingdom.

1
Occupational segregation is understood in this thesis as the phenomenon according to which women
and men are concentrated in different types of occupations.

13

Traditionally, women enter degrees that are considered feminine, such as nursing,
teaching or the social disciplines, characterized by lower demand and wages. This dissertation
tries to test the hypothesis that women might tend to choose traditionally female degrees due
to a gender bias that signals this type of careers as appropriate for their gender. Particularly, it
looks at a possible existence of differentiated stimuli in encouraging girls and boys to develop
math, technology and science skills during childhood. Although there is no particular proof
that the construction of gender identity can determine the degree choice of women, at least
there seems to be a trend between female occupations and lower wages. The following data
from the United States and the United Kingdom illustrates this relationship (although
subsequent chapters will only deal with data from the United Kingdom).

TABLE 1:
Science and Engineering graduate students in the USA, by field of study and sex
Year: 2008


Total
Female
Male
Female/Total
Total
529 275

231 997
297 278
43.83%
Science minus social and behavioral sciences
and multidisciplinary studies
347 336
119 012
228 324
34.26%
Science
391 419
200 460
190 959
51.21%
Natural science
138 527
67 179
71 348
48.50%
Mathematics
21 400
7 751
13 649
36.22%
Computer sciences
49 553
12 545
37 008
25.32%
Social and behavioral sciences

176 380
109 751
66 629
62.22%
Multidisciplinary/interdisciplinary studies
5 559
3 234
2 325
58.18%
Engineering
137 856
31 537
106 319
22.88%
Source: National Science Foundation.





In the United States, for instance, most of the science graduates are women, but this
figure drops significantly –to just about a third- when social and behavioural sciences are
excluded from the group (National Science Foundation, 2011). In particular, computer science
and engineering show the lowest participation of women, as shown in Table 1. On the other
hand, natural sciences show a female participation rate close to gender parity due mainly to
women going into medicine, which is counterintuitive to the basic hypothesis, since medicine
is one of the most profitable career options, but it is also a career choice that, in theory, calls

14

for humanitarian service, a traditionally assumed female trait. This first example, drawn from
the United States, illustrates how degree choices are gender segregated, resulting in an
underrepresentation of women in technological subjects, such as computer sciences.




Considering all workers, the occupational wage gap in the United States also shows
some evidence supporting the hypothesis: for male-dominated occupations with a high
technological, scientific or mathematical component, such as computer science, actuary and
aerospace or nuclear engineering, the mean annual wage is about 95 000 USD (see graphics 1
and 2). These are also occupations in which female employment is below 27% and, in some
cases, not even registered. At the other extreme, for the female-dominated occupations (like
education, where women account for more than 80% of employment), the wages drop to half

15

or less than the previous ones. Compare the mean annual wage of computer hardware
engineers and teacher assistants: in both cases, about 90% of employment is gender
dominated, men in the case of computer engineers and women for teacher assistants, and the
first group makes four times as much as the latter. So if the gap is so significant, why aren’t
more women studying computer sciences? In fact, the only female-dominated occupation

16

that shows an average annual wage above 100 000 USD is the industrial organization
psychologists. Notice also that the wage gap is considerably high for close related occupations
with strong gender segregation: dental hygienists earn about 43% of a dentist’s wage, nurses
make between 23% and 38% of what a doctor makes, and paralegals and legal assistants earn
less than 40% of a lawyer’s wage. Dental hygienists, nurses and paralegals are all female-

dominated occupations, while dentists, doctors and lawyers are male-dominated. The latter
also illustrates how power relations might be reproduced through this occupational
segregation, since men are located in professions that represent more power, knowledge
(these professions require a degree), status and wealth (dentists, doctors, lawyers) than the
less trained women who work for them (dental hygienists, nurses and paralegals). Hence, it is
worth asking whether tradition and the performance of gender roles are the reason why
women choose not to invest in acquiring the degrees that will allow them to become dentists,
doctors and lawyers and have access to those higher wages.

Although numbers do not seem to be as clear for the United Kingdom (see graphics 3
and 4), data from this country also exemplifies the existence of gender segregation among
graduates in their fields of study: as it was the case with the United States, the percentage of
female graduates in the United Kingdom for subjects like mathematics, engineering,
technology and computer sciences is 35% or lower, while it tends to be high (above 60%) in
female dominated-fields of study, like education and languages. But the percentage of female
graduates in physics is above 40% and more than half of the graduates that obtained their
qualification in health (medicine and the likes), biology or veterinary were women, suggesting
less segregation than that observed in the United States, where these fields of study are still
dominated by men. Similarly, the wage gap between the female and male-dominated fields of
study is not as pronounced as the one observed for the United States
2
. Still, the male-
dominated fields of study mentioned before have mean annual salaries above the average for
all subjects (i.e., above 21 286 GBP), while the more traditional female-dominated ones like
education, social studies or nursing (subjects allied to medicine) show mean salaries below this


2
Note that the data for the wage gap in the UK refers to first degree leavers, so that it reflects the wage
gap of those people entering the labour market. This wage gap is expected to increase with time, as

women report more intermittence and fewer opportunities in employment. For illustrative purposes,
wages in this section are annual, but in the remaining of the thesis wages are measured hourly.

17

number. Again, subjects like medicine and veterinary sciences would be the exception,
showing a percentage of female graduates above 50% along high mean salaries.




18



Further, Graphic 5 depicts the gender composition of occupations for graduates in the
United Kingdom. Among them, working women are a minority in managerial occupations;
skilled trades; process, plants and machine operative and elementary occupations and are
considerably over represented in administrative and secretarial occupations and personal
services occupations, which include roles as care takers, a traditional female role. Finally, it’s
important to note that, for both genders, only about 12% of graduates work in non-
professional occupations, but among those who work in professional occupations, men are 1.5
times more likely to hold managerial occupations, suggesting again a gender segregation that
places men in the top positions.


19

Hence at first sight, there seems to be some evidence pointing to lower wages for the
female-dominated fields of study. At the same time, female occupational choices also seem to
deviate from the technological, mathematical careers, despite the fact that these offer higher
wages than the traditional female jobs, which makes it reasonable to consider the construction
of gender identity in childhood as a possible explanation for this occupational choice bias. All
of the above point to the main hypothesis that this research looks into: the possible existence
of an educational gender bias that discourages girls to learn, interact and feel comfortable
with technology, math and science, thus reinforcing the construction of patriarchal gender
identities in children. Hence, the interiorization of gender identity can help explain why girls
tend to choose “female prestigious” degrees, while the high-paying degrees remain male-

20

dominated. This hypothesis is tested in several steps: first, the dissertation explores whether
the environment a person grows in is associated with that person holding beliefs in gender
equality. Secondly, it tests whether these beliefs, the exposure to mathematics, science and
technology or other childhood experiences are associated with girls choosing high-earnings or
male-dominated degrees. And, finally, it tests whether these degrees actually imply higher
earnings for women. In all cases, the scope of the study is limited to the United Kingdom. The
reason for this is that the United Kingdom has long invested in rich datasets. In particular, the
1970 British Cohort Study, a longitudinal study that has traced a cohort since birth for almost
forty years, is, to my knowledge, the only longitudinal dataset with all the information
required to test the hypothesis (i.e., it has information on gender attitudes, technological
exposure at an early age, academic ability, degrees and earnings for the same individuals).
Therefore, the implied assumption is that the United Kingdom could serve as a reference in
understanding the underlying patterns and dynamics leading to degree choices for women.
Also, it is worth noting that the study is approached from an Economics framework, mainly the
identity economics and human capital models, although it intertwines with sociological and
feminist approaches.

The following section provides a review of some of the existing literature regarding the
different topics involved in the research and that influenced how the study is being
approached. This literature consists of economic models with applications in the United States
and United Kingdom, as well as other critical readings that complement or contest this
approach. Afterwards, the research is structured in three parts. Chapter 3 is an attempt to
test the hypothesis using an econometrics approach and provides, therefore, a quantitative
analysis on degree choice using data from the 1970 British Cohort Study (BCS). Chapter 4 is an
attempt to test the same hypothesis using data drawn from an online survey in which
respondents were allowed to share their own experiences, so that such data provides richer
information in terms of lived experiences and its possible sociological significance. Chapter 5
explores possible determinants for the female earnings function as well as a decomposition of
the gender wage gap using information drawn from the 1970 BCS. Chapter 6 concludes with a
summary of the most relevant research findings.



21

2. Literature review

““Similarly, then,” said I, “if it appears that the male and
the female sex have distinct qualifications for any arts
or pursuits, we shall affirm that they ought to be
assigned respectively to each. But if it appears that they
differ only in just this respect that the female bears and
the male begets, we shall say that no proof has yet
been produced that the woman differs from the man
for our purposes, but we shall continue to think that our
guardians and their wives ought to follow the same
pursuits.”” (Plato, 454 d-e)

This section presents a concise review of some of the economic literature, as well as
critiques and complementary readings, limiting the framework from which the main
hypothesis is stated. These are: the human capital model; the theory of discrimination; the
gender gap and gender occupational segregation; the skill-bias technological change
hypothesis and models on gender and identity. The models on human capital provide
understandings of the economic rationale underlying decisions on investment in education
and training, i.e., it provides the framework explaining how rational individuals choose how
much and what to educate themselves in. It also explains what the different characteristics
the market rewards individuals for are and, therefore, allows for an understanding of earnings
and their composition. Models on discrimination focus on explaining wage differences among
groups when there are no differences in productivity observed. These models help explain
why women are consistently paid less than men taking into account institutional and other
non-economic variables, such as tastes or dislikes for a particular group. In turn, models on
occupational segregation look deeper into the causes of the observed gender wage gap and
find that workers are allocated in different sectors according to the group they belong to,
which ultimately perpetuates the gender wage gap; while models of gender and identity try to
identify behavioural differences observed among women and men. That is, the latter focuses
on the background, experiences and preferences that may lead one group, women in this case,
to develop preferences that are not exclusively restricted to financial variables. Finally, the
skill-bias technological hypothesis serves as a basis to further explore the idea that exposure to

22

technology may result in higher productivity levels and wages, that is, this hypothesis informs
the presumption that mathematical, scientific and technological fields offer a higher standard
of living through higher wages. In the following sections, these models are presented
reproducing each of the authors’ original notations
3
.

2.1. The human capital model

According to the human capital model (Becker: 1993, first published in 1962),
education is the driving force of productivity and a determinant in explaining the wage
differentials: because in a competitive market real wages are determined by productivity, and
education enhances productivity, the decision of getting an education –or being trained-
depends on the gains of investing in it
4
. When a person decides to study, she is aware that
that particular education will provide her with a new set of skills that, in turn, will increase her
productivity. The market will reward this higher productivity with higher wages, creating an
incentive for people to invest in education. However, there are costs associated with it, such
as the direct costs of the investment –tuition fees, study materials, etc , the effort that the
person has to exert, and indirect costs of lost wages and opportunities foregone for leaving the
labour market to get an education. If the marginal gains of investing in human capital exceed
its marginal costs, people would then decide to carry on the investment. At the same time,
because the more able workers are more likely to succeed in training programs, the
complementarity between these variables leads to a wage differential: the most able workers
benefit from higher investment in human capital and, therefore, higher wages than the less
skilled and less trained ones. This means that the returns on human capital are increasing.



3
Since this section summarizes the different theories informing the hypothesis, it was decided to keep
the original notation given by each author. For each case, the variables are defined accordingly. This
implies that authors might differ on the notation used for a particular concept.
4

The decision to invest in education can be taken by the firm or the individual, both of which cases are
discussed below, including some of the critiques faced by this theory.

23

In the general form of the human capital model, Becker explains the decisions leading
to on-the-job training. In this model, firms decide to invest in training for their workers on the
initial period (t=0) if the following equilibrium condition is satisfied (Becker, 1993, p. 32):

(1) 











  







,

where:
MP
t
: marginal productivity of labour at time t,
W
t
: wage rate at time t,
k: outlay on training,
n: number of periods and
i: discount interest rate.

According to equation (1), a firm would invest in training up to the point where the
present value of the flow of marginal productivities of labour would equal their respective
marginal costs, which are given by the present value of the wages paid to the employees and
the cost of training. Because training also implies an opportunity cost of the production
foregone from spending time on training (


 

), Becker includes a new term C that
captures this opportunity cost and the cost of training, k. Further, by rearranging terms and
defining G as the present value of the net profits from training labour, the above condition
becomes (Becker, 1993, p.p. 32-33):

(2) 

  


  , with  









and   


 

 

That is, the marginal costs of training, expressed by the term 

 , must equal the
gains expected from it (

 ): if the net flow of expected marginal productivities of labour
was higher than the marginal cost of training, the firm would have an incentive to keep
investing up to where (2) is satisfied. On the other hand, if the present value of such net
revenues were lower than the costs of training, the firm will cut back on the investment in
human capital. Becker points out that G-C are the net returns from training, which implies
that MP
0
need not be equal to W

0
. In fact, MP
0
only equals W
0
if G equals C. Hence, the firm

24

might pay wages above the marginal productivity of labour during the training period if it
expects this training to result in higher future net profits. And, because workers would be paid
according to their productivities, those workers with higher net returns would receive higher
wages.

Further, Becker offers a variant of his model to explain schooling decisions. In this
version, a student’s net earnings, W, equal the differences between potential earnings, MP
0
,
and total costs, C, which again include both the opportunity cost of foregone earnings (MP
0
-
MP) and the direct costs of schooling (Becker, 1993, p. 52):

(3)  





  




 

Because the result is similar to the more general model, Becker draws parallel
conclusions:

“Thus schooling would steepen the age-earnings profile, mix together the
income and capital accounts, introduce the negative relation between the
permanent and current earnings of young persons, and (implicitly) provide for
depreciation on its capital.” (Becker, 1993, p. 52)

His arguments are as follows: because people give up earnings early in life to get some
schooling, the initial earnings are lower than if no investment was done. At the same time,
schooling enhances productivities and thus increases future earnings, which is why the age-
earnings curve is steepened by schooling. The second argument refers to the
complementarity between labour and capital: schooling results in higher productivities of
labour associated with increasing returns on human capital. Thirdly, more time and effort put
into schooling are associated with higher opportunity costs that should reflect in much higher
returns in the future. And, finally, because the returns on schooling are a flux over time, it is
more profitable for younger people to invest in schooling than older people, simply because
they have more periods left after schooling from which they will collect these returns.
Therefore, as people grow older, investing in human capital becomes more costly and their
capital depreciates in time. In his empirical findings, Becker reports a rate of college return for

25

urban male whites in 1939 of about 14.5% and of about 13% for all male whites in 1949, using
data from the 1940 and 1950 Census in the United States, which show significant rates of

returns on college education (Becker: 1993, pp. 169-170).

In line with Becker’s model, Mincer (1970) showed that earning inequality increases as
the rate of return on education increases, so that the earning gap widens for higher levels of
ability and schooling. In this theoretical model, the ratio of annual earnings between two
individuals with a constant flow of earnings would be given by (Mincer, 1970, p. 7):

(4) 
























,

where:
k
2,1
: ratio of annual earnings between individuals 1 and 2,
E
Si
: annual earnings of individual i,
r: discount rate,
S
i
: years of schooling of individual i, and
n
i
: years of working life of individual i.

Further, if people work for a considerable amount of periods (n
1
= n
2
), individual 1 has
no schooling (s
1
=0) and individual 2 has a level of schooling s (s
2
=s), the ratio of annual
earnings tend to 




, i.e., the excess earnings reported by individual 2 are due entirely to
her investment in schooling. Taking this limit and applying a logarithmic transformation allows
solving for a rate of return to schooling (Mincer, 1970, p. 7):

(5) 



 

Hence, Mincer shows three relevant arguments in explaining the wage distribution.
First, he shows that “percentage differentials in earnings [are] a linear function of time spent at
school” (Mincer, 1970, p. 7). That is, investment in human capital results in higher earnings,
even when it implies postponing the years of work, since schooling is the major determinant of
wage differentials. The relationship is linear, as depicted in (5). Secondly, the exponential
components in (4) explains why, even if education was symmetrical, the earnings function